{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\n",
      "Requirement already satisfied: matplotlib in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (3.6.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (2.8.2)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (1.4.4)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (4.38.0)\r\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (9.2.0)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (21.3)\r\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (3.0.9)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (0.11.0)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (1.0.5)\r\n",
      "Requirement already satisfied: numpy>=1.19 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from matplotlib) (1.23.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /home/geantvert/.local/share/virtualenvs/kernl/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\r\n",
      "\u001B[33mWARNING: You are using pip version 21.3.1; however, version 22.3 is available.\r\n",
      "You should consider upgrading via the '/home/geantvert/.local/share/virtualenvs/kernl/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n"
     ]
    }
   ],
   "source": [
    "! pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict()\n",
    "with open(\"./data.csv\") as f:\n",
    "    for l in f:\n",
    "        name, batch, seq_len, latency = l.split(\",\")\n",
    "        data[(name, int(batch), int(seq_len))] = float(latency.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "speedup = dict()\n",
    "for (name, batch, seq_len), latency in data.items():\n",
    "    if name == \"Baseline\":\n",
    "        continue\n",
    "    if seq_len not in [16, 128, 256, 384, 512]:\n",
    "        continue\n",
    "    key = (\"Baseline\", batch, seq_len)\n",
    "    if key not in data:\n",
    "        continue\n",
    "    baseline_latency = data[key]\n",
    "    if name == \"Kernl\":\n",
    "        name = \"Kernl (this project)\"\n",
    "    speedup[(name, batch, seq_len)] = baseline_latency / latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3589230/1318777340.py:37: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[b_index].set_xticklabels(xticklabels)\n",
      "/tmp/ipykernel_3589230/1318777340.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[b_index].set_yticklabels(y_ticks_labels)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2400x1600 with 2 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.dpi\"] = 200\n",
    "\n",
    "fig, ax = plt.subplots(2, figsize=(12, 8))\n",
    "width = 0.1\n",
    "\n",
    "engines = [\n",
    "    \"ONNX Runtime\",\n",
    "    \"AITemplate\",\n",
    "    \"TensorRT\",\n",
    "    \"Inductor\",\n",
    "    \"DeepSpeed\",\n",
    "    \"Cuda graphs\",\n",
    "    \"Nvfuser\",\n",
    "    \"Kernl (this project)\",\n",
    "]\n",
    "colors = [\"#ede15b\", \"#bdcf32\", \"#87bc45\", \"#ea5545\", \"#ef9b20\", \"#f46a9b\", \"#b33dc6\", \"#27aeef\", \"#edbf33\"]\n",
    "\n",
    "batches = [1, 8]\n",
    "seq_len = [16, 128, 256, 384, 512]\n",
    "legend_box = list()\n",
    "\n",
    "for idx_engine, (engine, color) in enumerate(zip(engines, colors)):\n",
    "    for b_index, b in enumerate(batches):\n",
    "        median_latencies = list()\n",
    "        for s_l in seq_len:\n",
    "            latency = speedup[(engine, b, s_l)]\n",
    "            median_latencies.append(latency)\n",
    "        rects = ax[b_index].bar(\n",
    "            [v + idx_engine * width for v in range(len(median_latencies))],\n",
    "            median_latencies,\n",
    "            width,\n",
    "            color=color,\n",
    "            alpha=0.9,\n",
    "        )\n",
    "        if b_index == 0:\n",
    "            legend_box.append(rects[0])\n",
    "\n",
    "for b_index, b in enumerate(batches):\n",
    "    ax[b_index].axhline(y=1, color=\"r\", linestyle=\"-\", alpha=0.3)\n",
    "    xticklabels = [f\"({b}, {s_l})\" for s_l in seq_len]\n",
    "    ax[b_index].set_xticklabels(xticklabels)\n",
    "    ax[b_index].set_xticks([v + width * len(engines) / 2 for v in range(len(xticklabels))])\n",
    "\n",
    "    y_ticks_labels = [\"baseline\"] + [f\"{i}X\" for i in range(2, 13, 2)]\n",
    "    ax[b_index].set_yticklabels(y_ticks_labels)\n",
    "    y_ticks = [1] + list(range(2, 13, 2))\n",
    "    ax[b_index].set_yticks(y_ticks)\n",
    "\n",
    "\n",
    "fig.suptitle(\"Speedup over Hugging Face baseline\\n(Bert base, AMP fp16)\", fontsize=\"xx-large\")\n",
    "ax[0].set_title(\"batch size = 1 ⬇\")\n",
    "ax[1].set_title(\"batch size = 8 ⬇\")\n",
    "\n",
    "fig.supxlabel(\"input shape\\n(batch size, sequence length)\", fontsize=\"x-large\")\n",
    "fig.supylabel(\"speedup (x times faster)\", fontsize=\"x-large\")\n",
    "fig.tight_layout(pad=1.5)\n",
    "fig.legend(\n",
    "    legend_box, engines, loc=\"center right\", ncols=1, bbox_to_anchor=(1.15, 0.5), shadow=True, title=\"inference engines\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
